In order to maximize spectrum efficiency and transmission speed in a wireless communication system, there is an on-going effort to apply MIMO (multi-input multi-output) technology to systems. There have been many cases recently in which MIMO technology hitherto mentioned only in theory was applied to actual systems to increase system performance. MIMO technology was employed in the recently commercialized IEEE 802.16e mobile-WiMAX system, as well as in the LTE system and IEEE 802.16m system, representative of 4G technology. However, since MIMO technology requires multiple antennas in the transmitter and is difficult to apply when the transmission power is low, it was not easy to use the MIMO technology for the up-link of a mobile terminal.
Recently, collaborative spatial multiplexing, which applies MIMO transmission such that multiple transmitters having one or two antennas operate as if one transmitter were using multiple antennas so as to provide a MIMO effect, was employed for the up-link of WiMAX, LTE, and IEEE 802.16m.
Collaborative spatial multiplexing (CSM) is a technique in which multiple terminals each having one or more transmitting antennas in a multi-user up-link system are allotted frequency resources to transmit data simultaneously.
Collaborative spatial multiplexing provides the advantages of increasing transmission efficiency of the system while decreasing the number of antennas in relation to the number of MIMO terminals, for lower complexity and hence lower costs in manufacturing terminals. Also, when collaborative spatial multiplexing is used in the up-link of a multi-user MIMO system, each user has an independent MCS (modulation and coding scheme) level, so that AMC (adaptive modulation and channel coding) is suitably performed in consideration of the users' channel statuses and QoS (quality of service). These procedures ensure a similar error performance between each user.
Collaborative spatial multiplexing in a multi-user system can thus increase transmission efficiency by transmitting the data streams for multiple users over the same resources, but since the transmission signals of all multi-users are collectively received at each receiving antenna of a base station, it is necessary to separate the spatially multiplexed signals at the base station.
Since collaborative spatial multiplexing involves operating multiple transmitters as if one transmitter were using multiple antennas, it can consider signal detection techniques for existing spatial multiplexing MIMO systems. Signal detection techniques for the receiver unit of a MIMO system using spatial multiplexing can be classified into linear signal detection techniques, non-linear signal detection techniques, quasi-optimal signal detection techniques, and optimal signal detection techniques.
ZF (zero-forcing) and MMSE (minimum mean square error) signal detection techniques, which are linear signal detection techniques, entail a low level of computational complexity and thus can be implemented in relatively simpler structures, but provide much lower performance compared to optimal signal detection techniques. Signal detection techniques of the OSIC (ordered successive interference cancellation) type, which are non-linear signal detection techniques, involve successively removing the detection signals according to a predetermined order of detection so as to reduce the effect of interference signals.
Signal detection techniques of the OSIC type entail higher levels of computational complexity compared to linear signal detection techniques, but provide higher performance compared to linear signal detection techniques. The performance, however, is much lower compared to optimal signal detection methods, which provide the most optimal performance. The ML (maximum likelihood) signal detection technique, which provides optimal performance, is to detect signals by substituting the candidate vector values of all transmittable signals to compute and compare squared Euclidean distances and selecting the minimum squared Euclidean distance. As such, increases in the number of transmitted data streams and in the order of modulation result in exponential increases in computational complexity, and hence these techniques entail very high computational complexity. Signal detection techniques that reduce the high computational complexity of the ML signal detection technique while maintaining a performance level similar to the optimal performance include QRM-MLD (maximum likelihood detection with QR decomposition), SD (sphere decoding), etc. The SD technique is to compute the squared Euclidean distance by substituting symbols of constellations existing within a limited distance. While this can significantly reduce the average complexity compared to the ML technique, it is a depth-first searching method, so that the maximum complexity cannot be predicted, and it is difficult to apply in real-life implementations.
Unlike the SD technique, QRM-MLD is a breadth-first search having a fixed maximum complexity. While this technique can provide almost the same level of performance as the ML signal detection technique if there are a sufficient number of candidate vectors, the performance is greatly reduced if there are a small number of candidate vectors.
Among the existing signal detection methods described above, non-linear detection techniques of the OSIC type and quasi-optimal signal detection techniques such as QRM-MLD and SD may entail differences in performance between different data streams, due to error propagation in the signal detection process, limitations concerning the substituted symbols, or limitations concerning the symbol candidate vectors. In the case of OSIC type signal detection techniques, when a previously detected symbol was erroneously detected in a process involving sequential detection of signals, error propagation may occur. This may cause a difference in signal detection performance between data streams. In the case of SD and QRM-MLD, the symbols that are transmittable over a data stream are limited and substituted in the process of detecting signals, and the squared Euclidean distances are generated using only the candidate symbols. Thus, similar to the OSIC technique, differences in signal detection performance may occur between data streams for SD and QRM-MLD as well. Signal detection techniques that generate such phenomena are unsuitable for application to collaborative spatial multiplexing. As described above, a multi-user MIMO system planning to use collaborative spatial multiplexing in its up-link is configured such that the error performance is similar for each user in consideration of the users' channel environments and QoS, but if an existing signal detection method were to be used which generates large differences in signal detection performance between data streams, then the configuration of MCS level would be useless. As such, existing non-linear detection techniques of the OSIC type and quasi-optimal signal detection techniques such as QRM-MLD, SD, etc., in which the error performances between data streams are affected by error propagation during the signal detection process and limitations in the signal detection algorithms, are not suitable for a multi-user MIMO system using collaborative spatial multiplexing.